Meat, Other — Stock Variation by country
A food balance sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply...
What the numbers show
Meat, Other — Stock Variation is currently reported for 94 countries. The highest value is 37 1000 t in China, mainland; the lowest is -31 1000 t in Mongolia.
The median across all reporting countries is 0 1000 t, and the mean is 0.3298 1000 t.
Over the past decade 16 countries rose and 26 fell. The largest increase was in Afghanistan (up 100.0%), and the largest decrease in Mongolia (down 3,200.0%).
Meat, Other — Stock Variation: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | China, mainland | 37 1000 t | 2023 | down 54.9% | volatile |
| 2 | China | 36 1000 t | 2023 | down 56.1% | volatile |
| 3 | Saudi Arabia | 8 1000 t | 2023 | — | volatile |
| 4 | Ethiopia | 3 1000 t | 2023 | down 75.0% | volatile |
| 5 | Belgium | 2 1000 t | 2023 | — | volatile |
| 5 | Ireland | 2 1000 t | 2023 | — | volatile |
| 7 | Germany | 1 1000 t | 2023 | — | volatile |
| 7 | France | 1 1000 t | 2023 | — | flat |
| 7 | Kyrgyzstan | 1 1000 t | 2023 | — | volatile |
| 7 | Oman | 1 1000 t | 2023 | down 50.0% | volatile |
| 7 | Thailand | 1 1000 t | 2023 | — | volatile |
| 12 | Afghanistan | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Angola | 0 1000 t | 2023 | — | volatile |
| 12 | Australia | 0 1000 t | 2023 | — | volatile |
| 12 | Austria | 0 1000 t | 2023 | — | flat |
| 12 | Burkina Faso | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Bulgaria | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Belarus | 0 1000 t | 2023 | — | volatile |
| 12 | Brazil | 0 1000 t | 2023 | — | volatile |
| 12 | Switzerland | 0 1000 t | 2023 | — | volatile |
| 12 | Chile | 0 1000 t | 2023 | — | volatile |
| 12 | Cote d'Ivoire | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Cameroon | 0 1000 t | 2023 | — | volatile |
| 12 | Congo | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Colombia | 0 1000 t | 2023 | — | volatile |
| 12 | Czechia | 0 1000 t | 2023 | — | volatile |
| 12 | Denmark | 0 1000 t | 2023 | — | volatile |
| 12 | Algeria | 0 1000 t | 2023 | — | volatile |
| 12 | Egypt | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Spain | 0 1000 t | 2023 | — | volatile |
| 12 | Finland | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | United Kingdom | 0 1000 t | 2023 | — | volatile |
| 12 | Ghana | 0 1000 t | 2023 | up 100.0% | flat |
| 12 | Guinea | 0 1000 t | 2023 | — | volatile |
| 12 | Greece | 0 1000 t | 2023 | — | volatile |
| 12 | Croatia | 0 1000 t | 2023 | — | volatile |
| 12 | Haiti | 0 1000 t | 2023 | — | volatile |
| 12 | Hungary | 0 1000 t | 2023 | — | flat |
| 12 | Indonesia | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Italy | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Kazakhstan | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Kenya | 0 1000 t | 2023 | down 100.0% | flat |
| 12 | Kuwait | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Liberia | 0 1000 t | 2023 | — | volatile |
| 12 | Lithuania | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Morocco | 0 1000 t | 2023 | — | volatile |
| 12 | Mexico | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Malta | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Mauritania | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | New Caledonia | 0 1000 t | 2023 | — | volatile |
| 12 | Niger | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Nigeria | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Norway | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | New Zealand | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Peru | 0 1000 t | 2023 | — | volatile |
| 12 | Philippines | 0 1000 t | 2023 | down 100.0% | flat |
| 12 | Poland | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Portugal | 0 1000 t | 2023 | — | flat |
| 12 | Qatar | 0 1000 t | 2023 | — | volatile |
| 12 | Romania | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Russia | 0 1000 t | 2023 | — | volatile |
| 12 | Rwanda | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Senegal | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Sierra Leone | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Slovakia | 0 1000 t | 2023 | — | volatile |
| 12 | Sweden | 0 1000 t | 2023 | — | volatile |
| 12 | Syria | 0 1000 t | 2023 | — | volatile |
| 12 | Ukraine | 0 1000 t | 2023 | — | volatile |
| 12 | Uruguay | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | United States | 0 1000 t | 2023 | — | volatile |
| 12 | Uzbekistan | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | South Africa | 0 1000 t | 2023 | — | volatile |
| 12 | Zambia | 0 1000 t | 2023 | — | volatile |
| 12 | Zimbabwe | 0 1000 t | 2023 | up 100.0% | volatile |
| 12 | Melanesia | 0 1000 t | 2023 | — | volatile |
| 12 | Democratic Republic of the Congo | 0 1000 t | 2023 | — | volatile |
| 12 | Caribbean | 0 1000 t | 2023 | — | volatile |
| 12 | China, Hong Kong SAR | 0 1000 t | 2023 | — | volatile |
| 12 | Iran (Islamic Republic of) | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Republic of Korea | 0 1000 t | 2023 | down 100.0% | flat |
| 12 | Russian Federation | 0 1000 t | 2023 | — | volatile |
| 12 | Syrian Arab Republic | 0 1000 t | 2023 | — | volatile |
| 12 | Australia and New Zealand | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | Côte d'Ivoire | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | United Republic of Tanzania | 0 1000 t | 2023 | down 100.0% | volatile |
| 12 | United Kingdom of Great Britain and Northern Ireland | 0 1000 t | 2023 | — | volatile |
| 181 | Argentina | -1 1000 t | 2023 | — | volatile |
| 181 | Botswana | -1 1000 t | 2023 | — | volatile |
| 181 | Netherlands (Kingdom of the) | -1 1000 t | 2023 | — | volatile |
| 184 | United Arab Emirates | -2 1000 t | 2023 | up 66.7% | volatile |
| 185 | Canada | -3 1000 t | 2023 | — | volatile |
| 185 | Democratic People's Republic of Korea | -3 1000 t | 2018 | up 81.2% | volatile |
| 187 | Cuba | -20 1000 t | 2019 | — | volatile |
| 188 | Mongolia | -31 1000 t | 2023 | down 3,200.0% | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- World 20 1000 t
- Asia 13 1000 t
- Europe 6 1000 t
- Western Asia 6 1000 t
- European Union (27) 5 1000 t
- Eastern Asia 5 1000 t
- Low Income Food Deficit Countries (LIFDCs) 5 1000 t
- Least Developed Countries (LDCs) 4 1000 t
- Africa 3 1000 t
- Western Europe 3 1000 t
- Eastern Africa 3 1000 t
- Northern Europe 3 1000 t
- Central Asia 1 1000 t
- South-Eastern Asia 1 1000 t
- Oceania 0 1000 t
- South America 0 1000 t
- Small island developing States (SIDS) 0 1000 t
- Eastern Europe 0 1000 t
- Northern Africa 0 1000 t
- Western Africa 0 1000 t
- Central America 0 1000 t
- Southern Europe 0 1000 t
- Middle Africa 0 1000 t
- Southern Asia 0 1000 t
- United States of America 0 1000 t
- Southern Africa -1 1000 t
- Northern America -2 1000 t
- Americas -3 1000 t
- Land Locked Developing Countries (LLDCs) -27 1000 t
- Net Food Importing Developing Countries (NFIDCs) -28 1000 t
About this data
A food balance sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per capita food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.